Impact of the metabolic syndrome and its individual components on risk and severity of coronary heart disease

Abstract

The clinical use of criteria for metabolic syndrome (MetS) and its individual components with respect to risk prediction of coronary heart disease (CHD) remains uncertain. In this study, we investigated whether and to what extent MetS and its individual components were related to risk for CHD. A total of 1,028 subjects, who had undergone coronary angiography or were diagnosed as acute myocardial infarction, were selected according to inclusion criteria. MetS was diagnosed with National Cholesterol Education Program Adult Treatment Panel III (NCEP ATPIII) criteria. CHD was diagnosed with clinical data and confirmed by coronary angiography. The severity of coronary atherosclerosis was estimated by CHD Gensini cumulative index. All the patients were aged 33-87 years. The results showed that the age- and sex-adjusted odds ratios (ORs) for CHD in different individual components of MetS were as follows: low-high density lipoprotein (low-HDL), 3.15 (1.94-5.12); high-fasting plasma glucose (high-FPG), 2.26 (1.63-3.69); high-blood pressure (high-BP), 2.13 (1.38-3.29); high-triglycerides (high-TG), 1.55 (1.13-2.11); all P < 0.05, whereas high-body mass index (high-BMI), 0.75 (0.55-1.03) and high-waist circumference, 0.75 (0.51-1.10), both P > 0.05. Among all the components, the triad of low-HDL, high-FPG, and high-BP had the highest OR for CHD: 4.28 (3.12-5.87) (P < 0.001). MetS subjects had significant increases in number of disease vessel and CHD Gensini index (P < 0.001). When individual components of MetS were considered separately, groups with low-HDL, or high-FPG, or high-BP had significant increases in number of disease vessel and Gensini index (all P < 0.001). In conclusion, our present results demonstrated that individual components of MetS and their various combinations may have different contributions to CHD and the severity of coronary artery stenosis. Clinical focus should remain on establishing optimum-risk algorithms for CHD.